{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version: 1.4.0\n",
      "Keras version: 2.1.5\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "import tensorflow as tf\n",
    "print('TensorFlow version:', tf.__version__)\n",
    "print('Keras version:', keras.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import join\n",
    "import json\n",
    "import random\n",
    "import itertools\n",
    "import re\n",
    "import datetime\n",
    "import cairocffi as cairo\n",
    "import editdistance\n",
    "import numpy as np\n",
    "from scipy import ndimage\n",
    "import pylab\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "from keras import backend as K\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
    "from keras.layers import Input, Dense, Activation\n",
    "from keras.layers import Reshape, Lambda\n",
    "from keras.layers.merge import add, concatenate\n",
    "from keras.models import Model, load_model\n",
    "from keras.layers.recurrent import GRU\n",
    "from keras.optimizers import SGD\n",
    "from keras.utils.data_utils import get_file\n",
    "from keras.preprocessing import image\n",
    "import keras.callbacks\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "K.set_session(sess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get alphabet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max plate length in \"anpr_ocr__train\": 8\n",
      "Max plate length in \"anpr_ocr__train\": 8\n",
      "Letters in train and val do match\n",
      "Letters: 0 1 2 3 4 5 6 7 8 9 A B C E H K M O P T X Y\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "def get_counter(dirpath, tag):\n",
    "    dirname = os.path.basename(dirpath)\n",
    "    ann_dirpath = join(dirpath, 'ann')\n",
    "    letters = ''\n",
    "    lens = []\n",
    "    for filename in os.listdir(ann_dirpath):\n",
    "        json_filepath = join(ann_dirpath, filename)\n",
    "        ann = json.load(open(json_filepath, 'r'))\n",
    "        tags = ann['tags']\n",
    "        if tag in tags:\n",
    "            description = ann['description']\n",
    "            lens.append(len(description))\n",
    "            letters += description\n",
    "    print('Max plate length in \"%s\":' % dirname, max(Counter(lens).keys()))\n",
    "    return Counter(letters)\n",
    "c_val = get_counter('/data/anpr_ocr__train', 'val')\n",
    "c_train = get_counter('/data/anpr_ocr__train', 'train')\n",
    "letters_train = set(c_train.keys())\n",
    "letters_val = set(c_val.keys())\n",
    "if letters_train == letters_val:\n",
    "    print('Letters in train and val do match')\n",
    "else:\n",
    "    raise Exception()\n",
    "# print(len(letters_train), len(letters_val), len(letters_val | letters_train))\n",
    "letters = sorted(list(letters_train))\n",
    "print('Letters:', ' '.join(letters))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input data generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def labels_to_text(labels):\n",
    "    return ''.join(list(map(lambda x: letters[int(x)], labels)))\n",
    "\n",
    "def text_to_labels(text):\n",
    "    return list(map(lambda x: letters.index(x), text))\n",
    "\n",
    "def is_valid_str(s):\n",
    "    for ch in s:\n",
    "        if not ch in letters:\n",
    "            return False\n",
    "    return True\n",
    "\n",
    "class TextImageGenerator:\n",
    "    \n",
    "    def __init__(self, \n",
    "                 dirpath,\n",
    "                 tag,\n",
    "                 img_w, img_h, \n",
    "                 batch_size, \n",
    "                 downsample_factor,\n",
    "                 max_text_len=8):\n",
    "        \n",
    "        self.img_h = img_h\n",
    "        self.img_w = img_w\n",
    "        self.batch_size = batch_size\n",
    "        self.max_text_len = max_text_len\n",
    "        self.downsample_factor = downsample_factor\n",
    "        \n",
    "        img_dirpath = join(dirpath, 'img')\n",
    "        ann_dirpath = join(dirpath, 'ann')\n",
    "        self.samples = []\n",
    "        for filename in os.listdir(img_dirpath):\n",
    "            name, ext = os.path.splitext(filename)\n",
    "            if ext in ['.png', '.jpg']:\n",
    "                img_filepath = join(img_dirpath, filename)\n",
    "                json_filepath = join(ann_dirpath, name + '.json')\n",
    "                ann = json.load(open(json_filepath, 'r'))\n",
    "                description = ann['description']\n",
    "                tags = ann['tags']\n",
    "                if tag not in tags:\n",
    "                    continue\n",
    "                if is_valid_str(description):\n",
    "                    self.samples.append([img_filepath, description])\n",
    "        \n",
    "        self.n = len(self.samples)\n",
    "        self.indexes = list(range(self.n))\n",
    "        self.cur_index = 0\n",
    "        \n",
    "    def build_data(self):\n",
    "        self.imgs = np.zeros((self.n, self.img_h, self.img_w))\n",
    "        self.texts = []\n",
    "        for i, (img_filepath, text) in enumerate(self.samples):\n",
    "            img = cv2.imread(img_filepath)\n",
    "            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "            img = cv2.resize(img, (self.img_w, self.img_h))\n",
    "            img = img.astype(np.float32)\n",
    "            img /= 255\n",
    "            # width and height are backwards from typical Keras convention\n",
    "            # because width is the time dimension when it gets fed into the RNN\n",
    "            self.imgs[i, :, :] = img\n",
    "            self.texts.append(text)\n",
    "        \n",
    "    def get_output_size(self):\n",
    "        return len(letters) + 1\n",
    "    \n",
    "    def next_sample(self):\n",
    "        self.cur_index += 1\n",
    "        if self.cur_index >= self.n:\n",
    "            self.cur_index = 0\n",
    "            random.shuffle(self.indexes)\n",
    "        return self.imgs[self.indexes[self.cur_index]], self.texts[self.indexes[self.cur_index]]\n",
    "    \n",
    "    def next_batch(self):\n",
    "        while True:\n",
    "            # width and height are backwards from typical Keras convention\n",
    "            # because width is the time dimension when it gets fed into the RNN\n",
    "            if K.image_data_format() == 'channels_first':\n",
    "                X_data = np.ones([self.batch_size, 1, self.img_w, self.img_h])\n",
    "            else:\n",
    "                X_data = np.ones([self.batch_size, self.img_w, self.img_h, 1])\n",
    "            Y_data = np.ones([self.batch_size, self.max_text_len])\n",
    "            input_length = np.ones((self.batch_size, 1)) * (self.img_w // self.downsample_factor - 2)\n",
    "            label_length = np.zeros((self.batch_size, 1))\n",
    "            source_str = []\n",
    "                                   \n",
    "            for i in range(self.batch_size):\n",
    "                img, text = self.next_sample()\n",
    "                img = img.T\n",
    "                if K.image_data_format() == 'channels_first':\n",
    "                    img = np.expand_dims(img, 0)\n",
    "                else:\n",
    "                    img = np.expand_dims(img, -1)\n",
    "                X_data[i] = img\n",
    "                Y_data[i] = text_to_labels(text)\n",
    "                source_str.append(text)\n",
    "                label_length[i] = len(text)\n",
    "                \n",
    "            inputs = {\n",
    "                'the_input': X_data,\n",
    "                'the_labels': Y_data,\n",
    "                'input_length': input_length,\n",
    "                'label_length': label_length,\n",
    "                #'source_str': source_str\n",
    "            }\n",
    "            outputs = {'ctc': np.zeros([self.batch_size])}\n",
    "            yield (inputs, outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tiger = TextImageGenerator('/data/anpr_ocr__train', 'val', 128, 64, 8, 4)\n",
    "tiger.build_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text generator output (data which will be fed into the neutral network):\n",
      "1) the_input (image)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2) the_labels (plate number): K062ME84 is encoded as [15, 0, 6, 2, 16, 13, 8, 4]\n",
      "3) input_length (width of image that is fed to the loss function): 30 == 128 / 4 - 2\n",
      "4) label_length (length of plate number): 8\n"
     ]
    }
   ],
   "source": [
    "for inp, out in tiger.next_batch():\n",
    "    print('Text generator output (data which will be fed into the neutral network):')\n",
    "    print('1) the_input (image)')\n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        img = inp['the_input'][0, 0, :, :]\n",
    "    else:\n",
    "        img = inp['the_input'][0, :, :, 0]\n",
    "    \n",
    "    plt.imshow(img.T, cmap='gray')\n",
    "    plt.show()\n",
    "    print('2) the_labels (plate number): %s is encoded as %s' % \n",
    "          (labels_to_text(inp['the_labels'][0]), list(map(int, inp['the_labels'][0]))))\n",
    "    print('3) input_length (width of image that is fed to the loss function): %d == %d / 4 - 2' % \n",
    "          (inp['input_length'][0], tiger.img_w))\n",
    "    print('4) label_length (length of plate number): %d' % inp['label_length'][0])\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss and train functions, network architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ctc_lambda_func(args):\n",
    "    y_pred, labels, input_length, label_length = args\n",
    "    # the 2 is critical here since the first couple outputs of the RNN\n",
    "    # tend to be garbage:\n",
    "    y_pred = y_pred[:, 2:, :]\n",
    "    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)\n",
    "\n",
    "\n",
    "def train(img_w, load=False):\n",
    "    # Input Parameters\n",
    "    img_h = 64\n",
    "\n",
    "    # Network parameters\n",
    "    conv_filters = 16\n",
    "    kernel_size = (3, 3)\n",
    "    pool_size = 2\n",
    "    time_dense_size = 32\n",
    "    rnn_size = 512\n",
    "\n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        input_shape = (1, img_w, img_h)\n",
    "    else:\n",
    "        input_shape = (img_w, img_h, 1)\n",
    "        \n",
    "    batch_size = 32\n",
    "    downsample_factor = pool_size ** 2\n",
    "    tiger_train = TextImageGenerator('/data/anpr_ocr__train', 'train', img_w, img_h, batch_size, downsample_factor)\n",
    "    tiger_train.build_data()\n",
    "    tiger_val = TextImageGenerator('/data/anpr_ocr__train', 'val', img_w, img_h, batch_size, downsample_factor)\n",
    "    tiger_val.build_data()\n",
    "\n",
    "    act = 'relu'\n",
    "    input_data = Input(name='the_input', shape=input_shape, dtype='float32')\n",
    "    inner = Conv2D(conv_filters, kernel_size, padding='same',\n",
    "                   activation=act, kernel_initializer='he_normal',\n",
    "                   name='conv1')(input_data)\n",
    "    inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max1')(inner)\n",
    "    inner = Conv2D(conv_filters, kernel_size, padding='same',\n",
    "                   activation=act, kernel_initializer='he_normal',\n",
    "                   name='conv2')(inner)\n",
    "    inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max2')(inner)\n",
    "\n",
    "    conv_to_rnn_dims = (img_w // (pool_size ** 2), (img_h // (pool_size ** 2)) * conv_filters)\n",
    "    inner = Reshape(target_shape=conv_to_rnn_dims, name='reshape')(inner)\n",
    "\n",
    "    # cuts down input size going into RNN:\n",
    "    inner = Dense(time_dense_size, activation=act, name='dense1')(inner)\n",
    "\n",
    "    # Two layers of bidirecitonal GRUs\n",
    "    # GRU seems to work as well, if not better than LSTM:\n",
    "    gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(inner)\n",
    "    gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(inner)\n",
    "    gru1_merged = add([gru_1, gru_1b])\n",
    "    gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)\n",
    "    gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)\n",
    "\n",
    "    # transforms RNN output to character activations:\n",
    "    inner = Dense(tiger_train.get_output_size(), kernel_initializer='he_normal',\n",
    "                  name='dense2')(concatenate([gru_2, gru_2b]))\n",
    "    y_pred = Activation('softmax', name='softmax')(inner)\n",
    "    Model(inputs=input_data, outputs=y_pred).summary()\n",
    "\n",
    "    labels = Input(name='the_labels', shape=[tiger_train.max_text_len], dtype='float32')\n",
    "    input_length = Input(name='input_length', shape=[1], dtype='int64')\n",
    "    label_length = Input(name='label_length', shape=[1], dtype='int64')\n",
    "    # Keras doesn't currently support loss funcs with extra parameters\n",
    "    # so CTC loss is implemented in a lambda layer\n",
    "    loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])\n",
    "\n",
    "    # clipnorm seems to speeds up convergence\n",
    "    sgd = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)\n",
    "\n",
    "    if load:\n",
    "        model = load_model('./tmp_model.h5', compile=False)\n",
    "    else:\n",
    "        model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)\n",
    "\n",
    "    # the loss calc occurs elsewhere, so use a dummy lambda func for the loss\n",
    "    model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd)\n",
    "    \n",
    "    if not load:\n",
    "        # captures output of softmax so we can decode the output during visualization\n",
    "        test_func = K.function([input_data], [y_pred])\n",
    "\n",
    "        model.fit_generator(generator=tiger_train.next_batch(), \n",
    "                            steps_per_epoch=tiger_train.n,\n",
    "                            epochs=1, \n",
    "                            validation_data=tiger_val.next_batch(), \n",
    "                            validation_steps=tiger_val.n)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model description and training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next block will take about 30 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "the_input (InputLayer)          (None, 128, 64, 1)   0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 128, 64, 16)  160         the_input[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max1 (MaxPooling2D)             (None, 64, 32, 16)   0           conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2 (Conv2D)                  (None, 64, 32, 16)   2320        max1[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "max2 (MaxPooling2D)             (None, 32, 16, 16)   0           conv2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 32, 256)      0           max2[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "dense1 (Dense)                  (None, 32, 32)       8224        reshape[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "gru1 (GRU)                      (None, 32, 512)      837120      dense1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gru1_b (GRU)                    (None, 32, 512)      837120      dense1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 32, 512)      0           gru1[0][0]                       \n",
      "                                                                 gru1_b[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gru2 (GRU)                      (None, 32, 512)      1574400     add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "gru2_b (GRU)                    (None, 32, 512)      1574400     add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 32, 1024)     0           gru2[0][0]                       \n",
      "                                                                 gru2_b[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense2 (Dense)                  (None, 32, 23)       23575       concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "softmax (Activation)            (None, 32, 23)       0           dense2[0][0]                     \n",
      "==================================================================================================\n",
      "Total params: 4,857,319\n",
      "Trainable params: 4,857,319\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/1\n",
      "10298/10298 [==============================] - 1219s 118ms/step - loss: 0.3557 - val_loss: 1.7434e-04\n"
     ]
    }
   ],
   "source": [
    "model = train(128, load=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function to decode neural network output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For a real OCR application, this should be beam search with a dictionary\n",
    "# and language model.  For this example, best path is sufficient.\n",
    "\n",
    "def decode_batch(out):\n",
    "    ret = []\n",
    "    for j in range(out.shape[0]):\n",
    "        out_best = list(np.argmax(out[j, 2:], 1))\n",
    "        out_best = [k for k, g in itertools.groupby(out_best)]\n",
    "        outstr = ''\n",
    "        for c in out_best:\n",
    "            if c < len(letters):\n",
    "                outstr += letters[c]\n",
    "        ret.append(outstr)\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test on validation images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: B331EC36\n",
      "True: B331EC36\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: K122CA31\n",
      "True: K122CA31\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: M202XP51\n",
      "True: M202XP51\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: T431KA04\n",
      "True: T431KA04\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: X038BE92\n",
      "True: X038BE92\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: P441AM08\n",
      "True: P441AM08\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: T076CH77\n",
      "True: T076CH77\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: A252EM35\n",
      "True: A252EM35\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tiger_test = TextImageGenerator('/data/anpr_ocr__test', 'test', 128, 64, 8, 4)\n",
    "tiger_test.build_data()\n",
    "\n",
    "net_inp = model.get_layer(name='the_input').input\n",
    "net_out = model.get_layer(name='softmax').output\n",
    "\n",
    "for inp_value, _ in tiger_test.next_batch():\n",
    "    bs = inp_value['the_input'].shape[0]\n",
    "    X_data = inp_value['the_input']\n",
    "    net_out_value = sess.run(net_out, feed_dict={net_inp:X_data})\n",
    "    pred_texts = decode_batch(net_out_value)\n",
    "    labels = inp_value['the_labels']\n",
    "    texts = []\n",
    "    for label in labels:\n",
    "        text = ''.join(list(map(lambda x: letters[int(x)], label)))\n",
    "        texts.append(text)\n",
    "    \n",
    "    for i in range(bs):\n",
    "        fig = plt.figure(figsize=(10, 10))\n",
    "        outer = gridspec.GridSpec(2, 1, wspace=10, hspace=0.1)\n",
    "        ax1 = plt.Subplot(fig, outer[0])\n",
    "        fig.add_subplot(ax1)\n",
    "        ax2 = plt.Subplot(fig, outer[1])\n",
    "        fig.add_subplot(ax2)\n",
    "        print('Predicted: %s\\nTrue: %s' % (pred_texts[i], texts[i]))\n",
    "        img = X_data[i][:, :, 0].T\n",
    "        ax1.set_title('Input img')\n",
    "        ax1.imshow(img, cmap='gray')\n",
    "        ax1.set_xticks([])\n",
    "        ax1.set_yticks([])\n",
    "        ax2.set_title('Activations')\n",
    "        ax2.imshow(net_out_value[i].T, cmap='binary', interpolation='nearest')\n",
    "        ax2.set_yticks(list(range(len(letters) + 1)))\n",
    "        ax2.set_yticklabels(letters + ['blank'])\n",
    "        ax2.grid(False)\n",
    "        for h in np.arange(-0.5, len(letters) + 1 + 0.5, 1):\n",
    "            ax2.axhline(h, linestyle='-', color='k', alpha=0.5, linewidth=1)\n",
    "        \n",
    "        #ax.axvline(x, linestyle='--', color='k')\n",
    "        plt.show()\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
